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Running Local Models with Careti: What You Need to Know

Cline is a powerful AI coding assistant that uses tool calls to help you write, analyze, and modify code. Running models locally can save API costs, but there are important trade-offs. Local models are far less reliable at using the essential tools that make Cline effective.

Why Local Models Are Different

When you run a "local version" of a model, you're actually running a heavily simplified copy of the original. This process--called distillation--is like compressing a master chef's knowledge into a basic cookbook. You keep simple recipes but lose complex techniques and intuition.

Local models are trained to mimic larger ones, but typically retain only about 1-26% of the original model's capacity. That massive reduction means:

• Reduced ability to understand complex context
• Weaker multi-step reasoning
• Limited tool use
• Simplified decision-making

Think of it like running your development environment on a calculator instead of a computer. Basic tasks may work, but complex tasks become unreliable or impossible.

What Actually Happens

When running local models with Cline:

Performance Impact

• Responses are 5-10x slower than cloud services.
• System resources (CPU, GPU, RAM) are heavily used.
• Your computer may become less responsive for other tasks.

Tool Reliability Issues

• Code analysis is less accurate.
• File operations may be unreliable.
• Browser automation is reduced.
• Terminal commands fail more often.
• Complex multi-step tasks often break.

Hardware Requirements

At minimum, you'll need:

• A modern GPU with 8GB+ VRAM and AVX2 support (RTX 3070 or higher)
• 32GB+ system RAM
• Fast SSD storage
• Good cooling

Even with this hardware, you're still running a smaller, less capable version of the model.

Model Size	What You Get
7B model	Basic coding, limited tool use
14B model	Better coding, unstable tool use
32B model	Good coding, inconsistent tool use
70B model	Best local performance, expensive hardware required

In short, the cloud (API) versions are the full models. For example, the full DeepSeek-R1 model is 671B. Distilled local models are inherently "diluted" versions of the cloud models.

Practical Recommendations

Consider This Approach

1. Use cloud models for:
   • Complex development work
   • Tasks where tool reliability matters
   • Multi-step tasks
   • Critical code changes
2. Use local models for:
   • Simple code completion
   • Basic documentation
   • Cases where privacy is the top priority
   • Learning and experimentation

If You Must Go Local

• Start with smaller models
• Keep tasks simple and focused
• Save work frequently
• Be ready to switch to cloud models for complex tasks
• Monitor system resources

Common Issues

• "Tool execution failed": Local models struggle with complex tool chains. Simplify your prompts.
• "The target machine actively refused the connection": This usually means Ollama or LM Studio isn't running, or it's on a different port/address than configured in Cline. Double-check the Base URL in API provider settings.
• "There's a problem with Cline...": Increase the model's context length to the maximum.
• Slow or incomplete responses: Local models are often slower than cloud models, especially on weaker hardware. Try smaller models and expect much longer processing times.
• System stability: Watch GPU/CPU usage and temperatures.
• Context limits: Local models often have smaller context windows than cloud models. Break work into smaller chunks.

Looking Ahead

Local model capabilities are improving, but they still cannot fully replace cloud services--especially for Cline's tool-based features. Carefully evaluate your requirements and hardware before committing to a local-only setup.

Need Help?

• Join our Discord community and r/caret.
• Check the latest compatibility guides.
• Share experiences with other developers.

Remember: when in doubt, prioritize reliability over cost savings for critical development work.